An emerging research area in genetics is to detect associations between complex traits and rare variants (RVs) with next-generation sequencing data. Due to extremely low minor allele frequencies (MAFs) of RVs, many existing tests for common variants (CVs), such as the univariate test on each individual variant, most popular in genome-wide association studies (GWAS), may no longer be suitable. To boost statistical power, a common theme of existing association tests is to aggregate information across multiple RVs in a gene. With sequencing data, since the majority of RVs may not be causal, in which case most, if not all, existing association tests have severely deteriorating performance. We propose developing and evaluating an adaptive test that can maintain high power across various situations, including in the presence of opposite association directions and of many non-associated RVs. We will extend the proposed adaptive test to pathway analysis and multi-trait analysis. The developed methods will be applied to detect associations of RV-cardiovascular traits with the sequencing data from the CHARGE-S and ESP cohorts. We will develop and distribute software implementing the proposed methods.